Forecasting Stock Market Returns using Artificial Neural Networks: Novel approach

Abstract

In recent years, there has been an increase in the amount of literature on artificial neural network applications in the business and finance realms. In reality, the field of stock return forecasting has received a lot of attention. This is owing to the fact that monetary benefits will be large if artificial neural network applications succeed. Many research have shown that various types of artificial neural network topologies can be successfully used to predict stock returns. This study examines and evaluates various neural network research approaches that have been utilised to forecast stock returns in various journal papers. Modeling methodologies and literature suggestions are also collated and discussed. Artificial neural networks are an emerging and promising computational technique that will continue to be a difficult tool for future research, according to the findings.

Neural Network and Convolutional Neural Network (Artificial Intelligence) is used for detection of Forecasting Stock Market Return. It is observed through empirical experiments that the selected input variables were effective to predict stock market returns. The forecasting stock market is used by Neural Network and the Convolutional Neural Network to detect the return percentile. In this research we will describe the prediction of stock return by performing Artificial Intelligence on Jupiter simulation tool by using Implement the neural network. This research work is proposed for for casting and prediction of stock market return on the basis of artificial intelligence based data set and values for used as prediction value. 

 We are executing the research on the basis of last year’s data set of stock market returns both positive and negative so that we will apply the prediction model using artificial intelligence techniques and generate for casting report of stock market retunes for all particular client those who want to investment.